Cerebras

Cerebras is an AI infrastructure company that sells speed: it builds its own wafer-scale engine, the WSE-3, along with the CS-4 rack system, using a whole silicon wafer plus on-chip SRAM to sidestep the memory-bandwidth bottleneck that limits GPUs. It does not train foundation models of its own. What it hosts are third-party open weights such as GPT OSS and Qwen, served at 1000+ tokens per second through an OpenAI-compatible API.

Core capabilities

• Wafer-scale hardware, built in-house: WSE-3 Turbo fits 4,000 billion transistors and 900,000 AI-optimized cores on a single piece of silicon, replacing a GPU cluster with one wafer
• Extreme inference speed: GPT OSS 120B is rated at about 3000 tokens/s and Qwen 3.8 27B at about 1850 tokens/s
• Two-line migration: fully OpenAI-compatible, so changing base_url and api_key is all it takes to switch over and run an A/B comparison
• Third-party open-model hosting: the public API offers gpt-oss-120b and qwen-3.8-27b, while dedicated enterprise tiers can extend to a much larger catalog including Llama, Kimi, GLM and DeepSeek
• Enterprise deployment options: reserved dedicated capacity, custom weights, on-premises systems and a training cloud, with data residency support

Use cases Real-time voice conversation, generative UI and multi-turn agent tool loops, where latency gets amplified; also high-throughput batch inference on open models and public-sector or enterprise projects that need on-premises deployment and data residency. Teams that need very large closed models or self-serve fine-tuning are better served elsewhere.

Standout advantages On the same model, sustained throughput runs at roughly 10–30x a GPU setup (30x is the vendor claim, third-party reviews put it at 10–20x and up to 75x on the largest models), while precision stays full and no distillation is involved. Rival Groq is cheaper and has a wider catalog; Cerebras wins by pushing latency to the limit.

Editorial take The speed advantage is real and third parties can reproduce it, and a $0.35/M entry price plus OpenAI compatibility keeps the cost of experimenting tiny, which makes it the default inference substrate for latency-sensitive products. Three things come with it: no permanent free tier (the $5 credit expires after 30 days), only two models on the public API, and production-grade quotas that have to be negotiated in an enterprise contract.